Electrophysiology has produced a wealth of information concerning characteristic patterns of neural activity underlying movement control in non-human primates. Such patterns differentiate functional classes of neurons and illuminate neural computations underlying different stages of motor planning and execution. The scarcity of high-resolution electrophysiological recordings in humans has hindered such descriptions of brain activity during uniquely human acts such as speech production. The goal of this dissertation was to identify and quantitatively characterize canonical temporal profiles of neural activity measured using surface and depth electrocorticography electrodes while pre-surgical epilepsy patients read aloud monosyllabic utterances. An unsupervised iterative clustering procedure was combined with a novel Kalman filter-based trend analysis to identify characteristic activity time courses that occurred across multiple subjects. A nonlinear distance measure was used to emphasize similarity at key portions of the activity profiles, including signal peaks. Eight canonical activity patterns were identified. These activity profiles fell broadly into two classes: symmetric profiles in which activity rises and falls at approximately the same rate, and ramp profiles in which activity rises relatively quickly and falls off gradually. Distinct characteristic time courses were found during four different task stages: early processing of the orthographic stimulus, phonological-to-motor processing, motor execution, and auditory processing of self-produced speech, with activity offset ramps in earlier stages approximately matching activity onset rates in later stages. The addition of an anatomical constraint to the distance measure to encourage clusters to form within local brain regions did not significantly change results. The anatomically constrained results showed a further subdivision of the eight canonical activity patterns, with the subdivisions primarily stemming from sub-clusters that are anatomically distinct across different brain regions, but maintained the base activity pattern of their parent cluster from the analysis without the anatomically constrained distance measure. The analysis tools developed herein provide a powerful means for identifying and quantitatively characterizing the neural computations underlying human speech production and may apply to other cognitive and behavioral domains.

Crustaceans have been used extensively as models for studying the nervous system. Members of the Order Decapoda, particularly the larger species such as lobsters and crayfish, have large segmented abdomens that are positioned by tonic flexor and extensor muscles. Importantly, the innervation of these tonic muscles is known in some detail. Each abdominal segment in crayfish is innervated bilaterally by three sets of nerves. The anterior pair of nerves in each ganglion controls the swimmeret appendages and sensory supply. The middle pair of nerves innervates the tonic extensor muscles and the regional sensory supply. The superficial branch of the most posterior pair of nerves in each ganglion is exclusively motor and supplies the tonic flexor muscles of that segment. The extension and flexion motor nerves contain six motor neurons, each of which is different in axonal diameter and thus produces impulses of different amplitude. Motor programs controlling each muscle can be characterized by the identifiable motor neurons that are activated. Early work in this field discovered that specific central interneurons control the abdominal positioning motor neurons. These interneurons were first referred to as "command neurons" and later as "command elements." Stimulation of an appropriate command element causes a complex, widespread output involving dozens of motor neurons. The output can be patterned even though the stimulus to the command element is of constant interval. The command elements are identifiable cells. When a stimulus is repeated in a command element, from either the same individual or from different individuals, the output is substantially the same. This outcome depends upon several factors. First, the command elements are not only identifiable, but they make many synapses with other neurons, and the synapses are substantially invariant. There are separate flexion-producing and extension-producing command elements. Abdominal flexion-producing command elements excite other flexion elements and inhibit extensor command elements. The extension producing elements do the opposite. These interactions insure that interneurons of a particular class (flexion- or extension-producing) synaptically recruit perhaps twenty others of similar output, and that command elements promoting the opposing movements are inhibited. This strong reciprocity and the recruitment of similar command elements give a powerful motor program that appears to mimic behavior.

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Chronic ankle instability (CAI), a pathological condition characterized by repetitive bouts of the ankle giving way, commonly develops following a lateral ankle sprain injury. Individuals with CAI have been shown to exhibit deficits in postural control and alterations in movement dynamics, which have been suggested to be contributing factors to the recurrent injury paradigm. The purpose of this investigation was to conduct a comprehensive biomechanical analyses to examine the influence of CAI on postural control and movement dynamics during a single leg squat, side-cut task, and single leg landing on an inverted surface. Fifteen participants with CAI and fifteen participants without CAI completed the study following a between-subjects design, with limb serving as the repeated measure during the single leg squat. Each participant completed a single leg squat, side-cut task, and unexpected and expected single leg landings on a tilted surface. Results from the single leg squat and single leg landings on the tilted surface were analyzed using a 2 x 2 mixed-model ANOVA, while results from the side-cut task were analyzed using an independent samples t-test. Statistical significance was considered for all dependent variables when p < 0.05. Individuals with CAI demonstrated impaired postural control, as indicated by reduced time-to-boundary, during the single leg squat compared to controls. Altered ankle joint kinetics and increased sagittal plane hip joint stiffness were observed in the CAI group compared to controls. With regards to the single leg landings on the inverted surface, during the unexpected landing condition the CAI group displayed altered neuromuscular control and ankle kinematics. However, when the landing on the inverted surface as expected, the CAI group exhibited similar motor control strategies to the control group. Findings from this study indicate CAI alters postural control and movement dynamics during functional and dynamic movements, which may be used by researchers and clinicians to develop rehabilitation protocols to restore maladaptive movement patterns in individuals that develop CAI.

This research project develops a low-cost high-cycle fatigue (HCF) testing system comprised of an AC motor, variable frequency drive (VFD), eccentric cam, and feedback controller. The system acts as a forced harmonic oscillator leveraging mechanical resonance to vibrate a specimen at a frequency required to induce the testing's strain amplitudes. This system depends highly on the material being tested. As such, the controller incorporates material characteristics. A frequency sweep measures the strain amplitude to characterize the specimen. Additionally, other measurements such as acceleration can be used as a proxy control variables for strain. A function converts the control variable to frequency. This function tunes a proportional integral derivative (PID) controller to emphasize stable control. This function, coupled with a tuned PID controller, converts the correction update into a voltage signal that commands a motor speed to reach the desired strain amplitude. Testing showed that a longer feedback loop time of 5 seconds was necessary to adequately control the system since the control variables are oscillatory by nature and need to be averaged over time to estimate accurate updates. Also, specimens with low damping are more subject to transient effects; consequently, rapid updates degrade system performance. Overall, the system tested over 250,000 cycles and various specimens. The main limitation of the system is a maximum strain amplitude limited by the specific specimen resonant peak. However, adjusting the system's fixed displacement enables transferring more force to the specimen, changing the shape of the resonant peak.

The dynamics of wrist rotations are dominated by joint stiffness, which the neuromuscular system must account and compensate for when controlling wrist movements. Because wrist stiffness is anisotropic, movements in some directions require less torque than movements in others, creating opportunities to follow "paths of least resistance." Forearm pronation-supination (PS) can combine with wrist flexion-extension (FE) and radial-ulnar deviation (RUD) to allow the wrist to rotate in directions of least stiffness. Evaluating this hypothesis, and understanding the control of combined wrist and forearm rotations in general, requires a knowledge of the stiffness (the dominant mechanical impedance) encountered during combined wrist and forearm rotations. While wrist and forearm stiffness have been measured in isolation, there are no measurements of coupled wrist and the forearm stiffness. This study characterizes the passive stiffness of the wrist and forearm in combinations of FE, RUD, and PS. Using a wrist and forearm robot, we measured coupled wrist and forearm stiffness for 15° movements from neutral position in 10 young, healthy subjects. We found the stiffness in PS to be significantly smaller than the stiffness in RUD, but similar in magnitude to the stiffness in FE, indicating that the torque required to overcome stiffness in combinations of PS and FE is significantly smaller than the torque required to overcome stiffness in combinations of FE and RUD (assuming equal displacements). The coupled stiffness measured here will enable future studies to determine optimal paths and to compare these optimal paths to observed movements involving wrist and forearm rotations.

Wrist and forearm motion is governed both by its dynamics and the control strategies employed by the neuromuscular system to execute goal oriented movement. Two experiments were conducted to increase our understanding of wrist and forearm motion. The first experiment involved 10 healthy subjects executing planned movements to targets involving all three degrees of freedom (DOF) of the wrist and forearm, namely wrist flexion-extension (FE), wrist radial-ulnar deviation, and forearm pronation-supination (PS). A model of wrist and forearm dynamics was developed, and the recorded movements were fed into the model to analyze the movement torques. This resulted in the following key findings: 1) The main impedance torques affecting wrist and forearm movements are stiffness and gravity, with damping and inertial effects contributing roughly 10% of the total torque. 2) There is significant coupling between all degrees of freedom (DOF) of the wrist and forearm, with stiffness effects being the most coupled and inertial effects being the least coupled. 3) Neglecting these interaction torques results in significant error in the prediction of the torque required for wrist and forearm movements, suggesting that the neuromuscular system must account for coupling in movement planning. A second experiment was conducted in which 10 different healthy subjects pointed to targets arranged on a plane in front of the subjects. This pointing task required two DOF, but subjects were allowed to use all three DOF of the wrist and forearm. While subjects could have completed the task with FE and RUD alone, it was found that subjects recruited PS as well. Hypotheses regarding why subjects would recruit PS even though it was not necessary included the minimization of a number of cost functions (work, effort, potential energy, path length) as well as mechanical interaction between the DOF of the wrist and forearm. It was found that the pattern of PS recruitment predicted from the mechanical interaction hypothesis most closely resembled the observed pattern. According to this hypothesis, the neuromuscular system uses a simplified 2 DOF model of the joints most critical to the task (FE and RUD) to plan the task, while leaving the third DOF (PS) uncontrolled. The resulting interaction torques create the observed pattern of PS movement.

This study examined the impact of several noise conditions on speech articulator movements during a sentence repetition task. Sixty participants in three age groups ranging from 20 to 70 repeated a sentence under five noise conditions. Lower lip movements during production of a target sentence were used to compute the spatiotemporal index (STI). It was hypothesized that STI would be lower (indicating greater stability) in the silent baseline condition. There were changes in speech production under several of the noise conditions. The duration for the 1-talker condition was significantly shorter when compared to the silent condition, which could be due to the impact of the 1-talker noise on the attention of the speaker. The peak velocity of a selected closing gesture increased in all of the noise conditions compared to silence. It could be speculated that the repetitive and predictable nature of the speaking task allowed participants to easily filter out the noise while automatically increasing the velocity of lip movements, and consequently, the rate of speech. The STI in the pink noise and 6-talker conditions was lower than in the silent condition, which may be interpreted to reflect a steadier manner of speech production. This could be due to the fact that in the 6-speaker noise condition, the overall effect was more similar to continuous noise, and thus potentially less distracting than hearing a single speaker talking. The count of velocity peaks was unexpectedly lower in the noise conditions compared to speech in silence, suggesting a smoother pattern of articulator movement. The repetitiveness of the task may not require a high level of self-monitoring, resulting in speech output that was more automatic in the noise conditions. With the presentation of noise during a speaking task, the intensity increased due to the Lombard effect in all of the noise conditions. People communicate in noisy environments every day, and an increased understanding of the effects of noise on speech would have value from both theoretical and clinical perspectives.

Smoothness is a hallmark of healthy movement and has the potential to be used as a marker of recovery in rehabilitation settings. While much past research has focused on shoulder and elbow movements (reaching), little is known about movements of the wrist despite its importance in everyday life and its impairment in many neurological and biomechanical disorders. Our current lack of knowledge regarding wrist movement prevents us from improving current models, diagnosis, and treatment of wrist disorders. In particular, while movement smoothness is a well-known characteristic of reaching movements and may potentially be used to diagnose and monitor recovery from neurological impairments, little is known about the smoothness of wrist rotations. Therefore, because the smoothness of wrist rotations has not been characterized, it cannot be used as a marker for diagnosis and evaluation. This study examines the smoothness of wrist rotations in comparison to the known baseline of reaching movements. Subjects were asked to perform wrist and reaching movements under a variety of conditions, including different speed and direction. To measure movement smoothness, this study used an established metric of speed profile number of maxima and presents a novel method for characterizing smoothness by fitting a minimum-jerk trajectory to real movement data.The results show that 1) wrist rotations are significantly less smooth than reaching movements (p≤0.0016), 2) smoothness decreases significantly as speed decreases (p<0.0001), and 3) wrist movements exhibit a pattern of smoothness that varies significantly between targets and outbound/inbound movement directions (p<0.0001). Potential causes for results 1 and 3 are presented and tested by simulation or reference to prior studies, because these findings were previously unknown. The decrease in smoothness with speed (result 2) has been found in prior studies of smoothness in reaching and finger movements. The reasoning behind the first result is explored by testing whether the difference in smoothness between wrist and reaching movements was due to differences in mechanical, muscular, neural, or protocol-related properties. The reasoning behind the third result is explored by testing whether the difference in wrist direction was due to anisotropy in musculoskeletal dynamics or anisotropy in movement duration. The simulations show that the wrist’s bandwidth is greater than that of the arm, and that there is nonvoluntary power in the bandwidth of the wrist that would be low-pass filtered in reaching movements, indicating that at least some of the difference in smoothness between wrist and reaching movements is due to differences in mechanical properties. Differences in muscular, neural, or protocol-related properties (signal-dependent noise, proprioceptive acuity, and the speed requirements of the task, respectively) do not appear to be the cause of the difference in smoothness between wrist and reaching movements. Differences in wrist smoothness between movement directions appears to be related to differences in movement duration between directions.

The purpose of this research was to examine whether a transition schedule of contextual interference facilitated learning in retention and transfer equal to or better than random and blocked schedules among children. The author selected participants from the central coast of California and from youth activity leagues. The author selected children between the ages of 10 to 13 with a mean age of 11.5. There were a total of 36 subjects, half male and half female. Unfortunately, due to computer error, only data from 15 subjects were saved and available for analysis. Researchers randomly assigned participants to one of three groups: the random group, the blocked group, or the transition group. Each group performed 60 trials during the acquisition phase and practiced a total of 3 different arm patterns. All three groups practiced each pattern a total of 20 times during acquisition. The random group practiced each pattern in random fashion such that no one pattern was repeated more than twice in a row. The blocked group performed 20 trials of the green pattern, followed by 20 trials of the blue pattern, and lastly 20 trials of the red pattern. The transition group performed the first 24 trials in a blocked fashion, that is 8 trials of the green pattern were practiced, followed by 8 trials of the blue pattern, and then 8 trials of the red pattern. The group then practiced smaller blocks and performed 5 trials of each color. Another 9 trials were performed in a blocked fashion with 3 trials of each pattern. The final 12 trials were presented randomly to this group. Following acquisition, participants took an immediate retention test that was counter balanced following a 10 minute rest. The retention test consisted of 9 random trials of the three various patterns. Researchers gave a transfer test following the retention test, which consisted of six trials of a novel (white) pattern. Researchers tested all three groups one week later with a delayed retention and transfer test similar to the tests described above. One-way ANOVA analysis of the data revealed a significant movement time difference (F=4.28; P=.039) during the delayed retention test. The follow up Tukey test demonstrated that the transition group had a significantly faster movement time than the blocked group but that random group was not significantly different from either the blocked or transition group. The other retention and transfer tests revealed no significance, however the trend in the data suggest that with a bigger sample size, the transition group would demonstrate learning equal to or better than both random and blocked groups. Further research is needed in the area of transition practice schedules.